Research Paper#Inverse Reinforcement Learning, Dynamic Discrete Choice, Machine Learning, Statistical Inference🔬 ResearchAnalyzed: Jan 3, 2026 09:30
Efficient Inference for IRL and DDC Models
Published:Dec 30, 2025 18:41
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of efficient and statistically sound inference in Inverse Reinforcement Learning (IRL) and Dynamic Discrete Choice (DDC) models. It bridges the gap between flexible machine learning approaches (which lack guarantees) and restrictive classical methods. The core contribution is a semiparametric framework that allows for flexible nonparametric estimation while maintaining statistical efficiency. This is significant because it enables more accurate and reliable analysis of sequential decision-making in various applications.
Key Takeaways
- •Proposes a semiparametric framework for efficient inference in IRL and DDC models.
- •Achieves statistical efficiency while allowing for flexible nonparametric estimation.
- •Extends classical inference for DDC models to nonparametric rewards.
- •Provides a unified and computationally tractable approach to statistical inference in IRL.
Reference
“The paper's key finding is the development of a semiparametric framework for debiased inverse reinforcement learning that yields statistically efficient inference for a broad class of reward-dependent functionals.”